一个面向复杂任务的多机器人分布式协调系统

基于多智能体系统理论, 研究在非结构、不确定环境下面向复杂任务的多机器人分布式协调系统的实现原理、方法和技术. 提出的递阶混合式协调结构、基于网络的通讯模式和基于有限状态机的规划与控制集成方法, 充分考虑了复杂任务和真实自然环境的特点. 通过构建一个全实物的多移动机器人实验平台, 对规划、控制、传感、通讯、协调与合作的各关键技术进行了开发和集成, 使多机器人分布式协调技术的研究直接面向实际应用, 编队和物料搬运的演示实验结果展示了多机器人协调技术的广阔应用前景.     

A Real-Time Hybrid Architecture for Biped Humanoids with Active Vision Mechanisms

A real-time hybrid control architecture for biped humanoid robots is proposed. The architecture is modular and hierarchical. The main robot’s functionalities are organized in four parallel modules: perception, actuation, world-modeling, and hybrid control. Hybrid control is divided in three behavior-based hierarchical layers: the planning layer, the deliberative layer, and the reactive layer, which work in parallel and have very different response speeds and planning capabilities. The architecture allows: (1) the coordination of multiple robots and the execution of group behaviors without disturbing the robot’s reactivity and responsivity, which is very relevant for biped humanoid robots whose gait control requires real-time processing. (2) The straightforward management of the robot’s resources using resource multiplexers. (3) The integration of active vision mechanisms in the reactive layer under control of behavior-dependant value functions from the deliberative layer. This adds flexibility in the implementation of complex functionalities, such as the ones required for playing soccer in robot teams. The architecture is validated using simulated and real Nao humanoid robots. Passive and active behaviors are tested in simulated and real robot soccer setups. In addition, the ability to execute group behaviors in real- time is tested in international robot soccer competitions.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem

Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.     

A parallel processing architecture for sensor-based control of intelligent mobile robots

Parallel processing plays an important role in sensor-based control of intelligent mobile robots. This paper describes the design and implementation of a parallel processing architecture used for real-time, sensor-based control of mobile robots. This architecture takes the form of a network of sensing and control nodes, based on a novel module that we call Locally Intelligent Control Agent (LICA). It is a hybrid control architecture containing low-level feedback control loops and high-level decision making components. All the sensing, planning, and control tasks for intelligent control of a mobile robot are distributed across such a network, and operate in parallel. It has been used successfully in many experiments to perform planning and navigation tasks in real-time. Such a generic architecture can be readily applied to many diverse applications.     

基于运动性能分析的双臂冗余度机器人协调运动研究

为了提高双臂冗余度机器人在其交互工作空间中的协调运动能力,以ABB YuMi为例,提出了一种计算简便并且能够有效反映双臂协调运动灵活性的性能指标。利用D-H法建立了YuMi机器人的运动学模型,分析了双臂可操作度的分布情况,分别研究了两种协调运动方式的运动学约束关系以及相应的运动控制规律,基于灵活性分析构建了双臂协调装配电机转子与轴承以及字母绘制的任务,通过仿真和实验验证了本文双臂可操作度指标的有效性及协调运动规划方法的正确性。     

基于多模式交互的多移动机器人分布式合作系统

本文研究合作型多移动机器人系统的分布式控制方法.为了保证多机器人间合作的实 时性和高效率,采用了一种分组策略,并提出了将局部感知和组内通信相集成的多模式机器人 间交互方法.考虑到任务的复杂性和真实环境的非结构化特点,构建了将递阶规划技术与基于 行为的反应式控制相结合的递阶混合式协调结构,并采用有限状态机模型实现了规划层与行为 层的协调机制.合作垃圾清运的实验结果证明了上述方法的有效性.     

Distributed object transportation on a desired path based on Constrain and Move strategy

In this paper, a distributed strategy to move objects on different arbitrary paths in a 2D plane is proposed and analyzed. This algorithm which is based on Constrain and Move strategy [M.N. Ahmadabadi, E. Nakano, A Constrain and Move approach to distributed object manipulation, IEEE Trans. Robotics Automation 17 (2) (2001) 157], organizes the robots in two groups. The object manipulation task also is decomposed to two different tasks. The task given to one group is control of linear velocity and that assigned to the other group is control of angular velocity of the object. The independence of these tasks makes the design of the distributed architecture of the team possible. To calculate each robot's desired velocity, a simple method using Constrain and Move strategy and robot's local sensors is developed. To prevent small errors in the robot sensory system from affecting the system performance, limited compliance is assumed in robot arms. The basic behaviors of the robots are presented. Moreover, simulation results are given to verify the proposed strategy.     

Management system for distributed and hierarchical robot groups

This paper introduces a management system for robot groups with distributed and hierarchical architecture. In order to manage cooperation tasks of a great many robots, we put management systems on each work sub-area. A management system on a certain sub-area divides a task given by the upper sub-area into sub-tasks and gives the lower sub-areas those sub-tasks. With this architecture, we obtain distributed and hierarchical management system for the whole working area. We produced a simulation system and showed an example of task division and execution. We also produced a demonstration system.     

Optimized Stochastic Policies for Task Allocation in Swarms of Robots

We present a scalable approach to dynamically allocating a swarm of homogeneous robots to multiple tasks, which are to be performed in parallel, following a desired distribution. We employ a decentralized strategy that requires no communication among robots. It is based on the development of a continuous abstraction of the swarm obtained by modeling population fractions and defining the task allocation problem as the selection of rates of robot ingress and egress to and from each task. These rates are used to determine probabilities that define stochastic control policies for individual robots, which, in turn, produce the desired collective behavior. We address the problem of computing rates to achieve fast redistribution of the swarm subject to constraint(s) on switching between tasks at equilibrium. We present several formulations of this optimization problem that vary in the precedence constraints between tasks and in their dependence on the initial robot distribution. We use each formulation to optimize the rates for a scenario with four tasks and compare the resulting control policies using a simulation in which 250 robots redistribute themselves among four buildings to survey the perimeters.      

Active learning of inverse models with intrinsically motivated goal exploration in robots

We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which allows active learning of inverse models in high-dimensional redundant robots. This allows a robot to efficiently and actively learn distributions of parameterized motor skills/policies that solve a corresponding distribution of parameterized tasks/goals. The architecture makes the robot sample actively novel parameterized tasks in the task space, based on a measure of competence progress, each of which triggers low-level goal-directed learning of the motor policy parameters that allow to solve it. For both learning and generalization, the system leverages regression techniques which allow to infer the motor policy parameters corresponding to a given novel parameterized task, and based on the previously learnt correspondences between policy and task parameters.We present experiments with high-dimensional continuous sensorimotor spaces in three different robotic setups: (1) learning the inverse kinematics in a highly-redundant robotic arm, (2) learning omnidirectional locomotion with motor primitives in a quadruped robot, and (3) an arm learning to control a fishing rod with a flexible wire. We show that (1) exploration in the task space can be a lot faster than exploration in the actuator space for learning inverse models in redundant robots; (2) selecting goals maximizing competence progress creates developmental trajectories driving the robot to progressively focus on tasks of increasing complexity and is statistically significantly more efficient than selecting tasks randomly, as well as more efficient than different standard active motor babbling methods; (3) this architecture allows the robot to actively discover which parts of its task space it can learn to reach and which part it cannot.     

基于行为的机器人编队控制研究

建立了一种基于行为的机器人编队控制结构模型,该结构采用分层控制策略,全局控制器根据当前所有机器人的状态从一个有限状态机中选择下一步机器人的行为,将协调控制量发送给各机器人,各机器人再通过局部控制器对自身进行控制。该结构模型简单、易行,并且适用于机器人不同任务的需要,具有很高的灵活性,而且易于仿真实现。在此基础上,将一类机器人模型进行反馈线性化,再根据编队控制的要求,利用后推方法设计控制律。仿真结果表明这种结构模型和控制算法是有效的。     

Distributed Coordination in Heterogeneous Multi-Robot Systems

Coordination in multi-robot systems is a very active research field in Artificial Intelligence and Robotics, since through coordination one can achieve a more effective execution of the robots' tasks. In this paper we present an approach to distributed coordination of a multi-robot system that is based on dynamic role assignment. The approach relies on the broadcast communication of utility functions that define the capability for every robot to perform a task and on the execution of a coordination protocol for dynamic role assignment. The presented method is robust to communication failures and suitable for application in dynamic environments. In addition to experimental results showing the effectiveness of our approach, the method has been successfully implemented within the team of heterogeneous robots Azzurra Robot Team in a very dynamic hostile environment provided by the RoboCup robotic soccer competitions.     

基于自适应动态窗口改进细菌算法与移动机器人路径规划

针对移动机器人在复杂环境下的路径规划问题,提出一种新的自适应动态窗口改进细菌算法,并将新算法应用于移动机器人路径规划。改进细菌算法继承了细菌算法与动态窗口算法（dynamic window algorithm, DWA）在避障时的优点,能较好实现复杂环境中移动机器人静态和动态避障。该改进算法主要分三步完成移动机器人路径规划。首先,利用改进细菌趋化算法在静态环境中得到初始参考规划路径。接着,基于参考路径,机器人通过自身携带的传感器感知动态障碍物进行动态避障并利用自适应DWA完成局部动态避障路径规划。最后,根据移动机器人局部动态避障完成情况选择算法执行步骤,如果移动机器人能达到最终目标点,结束该算法,否则移动机器人再重回初始路径,直至到达最终目标点。仿真比较实验证明,改进算法无论在收敛速度还是路径规划精确度方面都有明显提升。     

Lifelong Adaptation in Heterogeneous Multi-Robot Teams: Response to Continual Variation in Individual Robot Performance

Generating teams of robots that are able to perform their tasks over long periods of time requires the robots to be responsive to continual changes in robot team member capabilities and to changes in the state of the environment and mission. In this article, we describe the L-ALLIANCE architecture, which enables teams of heterogeneous robots to dynamically adapt their actions over time. This architecture, which is an extension of our earlier work on ALLIANCE, is a distributed, behavior-based architecture aimed for use in applications consisting of a collection of independent tasks. The key issue addressed in L-ALLIANCE is the determination of which tasks robots should select to perform during their mission, even when multiple robots with heterogeneous, continually changing capabilities are present on the team. In this approach, robots monitor the performance of their teammates performing common tasks, and evaluate their performance based upon the time of task completion. Robots then use this information throughout the lifetime of their mission to automatically update their control parameters. After describing the L-ALLIANCE architecture, we discuss the results of implementing this approach on a physical team of heterogeneous robots performing proof-of-concept box pushing experiments. The results illustrate the ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous robot teams to continuing changes in the robot team member capabilities and in the environment.     

A PATH PLANNING METHOD BASED ON CELLULAR AUTOMATA FOR COOPERATIVE ROBOTS

A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.     

A decentralized approach for the conflict-free motion of multiple mobile robots

This article presents a novel approach to decentralized motion planning and conflict-resolution for multiple mobile robots. The proposed multi-robot motion planning is an on-line operation, based on cost wave propagation within a discretized configuration space-time. By use of the planning method, a framework for negotiation is developed, which permits quick decentralized and parallel decision making. The key objective of the negotiation procedure is dynamic assignment of robot motion priorities. Thus, robots involved in a local conflict situation cooperate in planning and execution of the lowest cost motion paths without application of any centralized components. The features required for individual and cooperative motion are embedded in a hybrid control architecture. Results obtained from realistic simulation of a multi-robot environment and also from experiments performed with two mobile robots demonstrate the flexibility and the efficiency of the proposed method.     

基于总体势减小的动态调度技术解决多机器人的路径规划

本文提出了一种解决多机器人路径规划与协调问题的新方法：基于总体势减小的优 先级动态调度策略．文中引入了总体势的概念,机器人从起始点向目标点运动过程中,始终 沿着总体势减小的方向进行,逐步引导机器人导航任务的完成．     

自主机器人多智能体软件架构及伴随行为机制

自主机器人是一类由计算机软件控制的信息物理系统，如何支持该类机器人在开放环境下的有效和协调运行是自主机器人控制软件（CSAR：Control Software of Autonomous Robot）研究与实践面临的一项重要挑战.本文基于组织理论的思想，采用Structure-in-5的组织架构模式，提出了基于多智能体的CSAR的软件架构MaRSA（Multi-agent Robotic Software Architecture），通过独立抽象CSAR的行为规划、分发、执行等软构件并显式加强这些构件间的交互，从而为自主机器人行为的有效规划和协调实施奠定架构基础；提出了基于MaRSA架构的伴随行为机制，从因果性、时序性和按需性三个方面建立了机器人观察行为和任务行为间的伴随关系，并基于分步规划和动态决策的思想设计并实现了伴随行为的自主决策算法DAAB（Decision Algorithm of Accompanying Behaviors）.论文分别在仿真环境和实际机器人环境下设计了对比性实验，结果表明，与主流的反应式行为决策算法和BDI式概率决策算法相比较，基于MaRSA和伴随行为机制的DAAB算法所生成的伴随行为规划在开放环境下具有可行性和更高效的执行效率.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.